OpenRaiser/Intern-Atlas huggingface dataset:
Exploiting Smoothness in Statistical Learning, Sequential Prediction, and Stochastic Optimization
Venue: arXiv cs.LG 2014
Year: 2014

In the last several years, the intimate connection between convex optimization and learning problems, in both statistical and sequential frameworks, has shifted the focus of algorithmic machine learning to examine this interplay. In particular, on one hand, this intertwinement brings forward new challenges in reassessment of the performance of learning algorithms including generalization and regret bounds under the assumptions imposed by convexity such as analytical properties of loss functions (e.g., Lipschitzness, strong convexity, and smoothness). On the other hand, emergence of datasets of an unprecedented size, demands the development of novel and more efficient optimization algorithms to tackle large-scale learning problems. The overarching goal of this thesis is to reassess the smoothness of loss functions in statistical learning, sequential prediction/online learning, and stochastic optimization and explicate its consequences. In particular we examine how smoothness of loss function could be beneficial or detrimental in these settings in terms of sample complexity, statistical consistency, regret analysis, and convergence rate, and investigate how smoothness can be leveraged to devise more efficient learning algorithms.
Deep Recurrent Neural Networks for Time Series Prediction
Venue: arXiv cs.NE 2014
Year: 2014

Ability of deep networks to extract high level features and of recurrent networks to perform time-series inference have been studied. In view of universality of one hidden layer network at approximating functions under weak constraints, the benefit of multiple layers is to enlarge the space of dynamical systems approximated or, given the space, reduce the number of units required for a certain error. Traditionally shallow networks with manually engineered features are used, back-propagation extent is limited to one and attempt to choose a large number of hidden units to satisfy the Markov condition is made. In case of Markov models, it has been shown that many systems need to be modeled as higher order. In the present work, we present deep recurrent networks with longer backpropagation through time extent as a solution to modeling systems that are high order and to predicting ahead. We study epileptic seizure suppression electro-stimulator. Extraction of manually engineered complex features and prediction employing them has not allowed small low-power implementations as, to avoid possibility of surgery, extraction of any features that may be required has to be included. In this solution, a recurrent neural network performs both feature extraction and prediction. We prove analytically that adding hidden layers or increasing backpropagation extent increases the rate of decrease of approximation error. A Dynamic Programming (DP) training procedure employing matrix operations is derived. DP and use of matrix operations makes the procedure efficient particularly when using data-parallel computing. The simulation studies show the geometry of the parameter space, that the network learns the temporal structure, that parameters converge while model output displays same dynamic behavior as the system and greater than .99 Average Detection Rate on all real seizure data tried.
Extra Gain:Improved Sparse Channel Estimation Using Reweighted l_1-norm Penalized LMS/F Algorithm
Venue: arXiv cs.IT 2014
Year: 2014

The channel estimation is one of important techniques to ensure reliable broadband signal transmission. Broadband channels are often modeled as a sparse channel. Comparing with traditional dense-assumption based linear channel estimation methods, e.g., least mean square/fourth (LMS/F) algorithm, exploiting sparse structure information can get extra performance gain. By introducing l_1-norm penalty, two sparse LMS/F algorithms, (zero-attracting LMSF, ZA-LMS/F and reweighted ZA-LMSF, RZA-LMSF), have been proposed [1]. Motivated by existing reweighted l_1-norm (RL1) sparse algorithm in compressive sensing [2], we propose an improved channel estimation method using RL1 sparse penalized LMS/F (RL1-LMS/F) algorithm to exploit more efficient sparse structure information. First, updating equation of RL1-LMS/F is derived. Second, we compare their sparse penalize strength via figure example. Finally, computer simulation results are given to validate the superiority of proposed method over than conventional two methods.
Autonomous requirements specification processing using natural language processing
Venue: arXiv cs.CL 2014
Year: 2014

We describe our ongoing research that centres on the application of natural language processing (NLP) to software engineering and systems development activities. In particular, this paper addresses the use of NLP in the requirements analysis and systems design processes. We have developed a prototype toolset that can assist the systems analyst or software engineer to select and verify terms relevant to a project. In this paper we describe the processes employed by the system to extract and classify objects of interest from requirements documents. These processes are illustrated using a small example.
Improving web search using contextual retrieval
Venue: arXiv cs.IR 2014
Year: 2014

Contextual retrieval is a critical technique for today's search engines in terms of facilitating queries and returning relevant information. This paper reports on the development and evaluation of a system designed to tackle some of the challenges associated with contextual information retrieval from the World Wide Web (WWW). The developed system has been designed with a view to capturing both implicit and explicit user data which is used to develop a personal contextual profile. Such profiles can be shared across multiple users to create a shared contextual knowledge base. These are used to refine search queries and improve both the search results for a user as well as their search experience. An empirical study has been undertaken to evaluate the system against a number of hypotheses. In this paper, results related to one are presented that support the claim that users can find information more readily using the contextual search system.
Permutation Models for Collaborative Ranking
Venue: arXiv cs.IR 2014
Year: 2014

We study the problem of collaborative filtering where ranking information is available. Focusing on the core of the collaborative ranking process, the user and their community, we propose new models for representation of the underlying permutations and prediction of ranks. The first approach is based on the assumption that the user makes successive choice of items in a stage-wise manner. In particular, we extend the Plackett-Luce model in two ways - introducing parameter factoring to account for user-specific contribution, and modelling the latent community in a generative setting. The second approach relies on log-linear parameterisation, which relaxes the discrete-choice assumption, but makes learning and inference much more involved. We propose MCMC-based learning and inference methods and derive linear-time prediction algorithms.
The Discrete Sell or Hold Problem with Constraints on Asset Values
Venue: arXiv cs.GT 2014
Year: 2014

The discrete sell or hold problem (DSHP), which is introduced in \cite{H12}, is studied under the constraint that each asset can only take a constant number of different values. We show that if each asset can take only two values, the problem becomes polynomial-time solvable. However, even if each asset can take three different values, DSHP is still NP-hard. An approximation algorithm is also given under this setting.
Efficient Enumeration of Induced Subtrees in a K-Degenerate Graph
Venue: arXiv cs.DS 2014
Year: 2014

In this paper, we address the problem of enumerating all induced subtrees in an input k-degenerate graph, where an induced subtree is an acyclic and connected induced subgraph. A graph G = (V, E) is a k-degenerate graph if for any its induced subgraph has a vertex whose degree is less than or equal to k, and many real-world graphs have small degeneracies, or very close to small degeneracies. Although, the studies are on subgraphs enumeration, such as trees, paths, and matchings, but the problem addresses the subgraph enumeration, such as enumeration of subgraphs that are trees. Their induced subgraph versions have not been studied well. One of few example is for chordless paths and cycles. Our motivation is to reduce the time complexity close to O(1) for each solution. This type of optimal algorithms are proposed many subgraph classes such as trees, and spanning trees. Induced subtrees are fundamental object thus it should be studied deeply and there possibly exist some efficient algorithms. Our algorithm utilizes nice properties of k-degeneracy to state an effective amortized analysis. As a result, the time complexity is reduced to O(k) time per induced subtree. The problem is solved in constant time for each in planar graphs, as a corollary.
Content-Level Selective Offloading in Heterogeneous Networks: Multi-armed Bandit Optimization and Regret Bounds
Venue: arXiv cs.IT 2014
Year: 2014

We consider content-level selective offloading of cellular downlink traffic to a wireless infostation terminal which stores high data-rate content in its cache memory. Cellular users in the vicinity of the infostation can directly download the stored content from the infostation through a broadband connection (e.g., WiFi), reducing the latency and load on the cellular network. The goal of the infostation cache controller (CC) is to store the most popular content in the cache memory such that the maximum amount of traffic is offloaded to the infostation. In practice, the popularity profile of the files is not known by the CC, which observes only the instantaneous demands for those contents stored in the cache. Hence, the cache content placement is optimised based on the demand history and on the cost associated to placing each content in the cache. By refreshing the cache content at regular time intervals, the CC gradually learns the popularity profile, while at the same time exploiting the limited cache capacity in the best way possible. This is formulated as a multi-armed bandit (MAB) problem with switching cost. Several algorithms are presented to decide on the cache content over time. The performance is measured in terms of cache efficiency, defined as the amount of net traffic that is offloaded to the infostation. In addition to theoretical regret bounds, the proposed algorithms are analysed through numerical simulations. In particular, the impact of system parameters, such as the number of files, number of users, cache size, and skewness of the popularity profile, on the performance is studied numerically. It is shown that the proposed algorithms learn the popularity profile quickly for a wide range of system parameters.
Visual Word Selection without Re-Coding and Re-Pooling
Venue: arXiv cs.CV 2014
Year: 2014

The Bag-of-Words (BoW) representation is widely used in computer vision. The size of the codebook impacts the time and space complexity of the applications that use BoW. Thus, given a training set for a particular computer vision task, a key problem is pruning a large codebook to select only a subset of visual words. Evaluating possible selections of words to be included in the pruned codebook can be computationally prohibitive; in a brute-force scheme, evaluating each pruned codebook requires re-coding of all features extracted from training images to words in the candidate codebook and then re-pooling the words to obtain a representation of each image, e.g., histogram of visual word frequencies. In this paper, a method is proposed that selects and evaluates a subset of words from an initially large codebook, without the need for re-coding or re-pooling. Formulations are proposed for two commonly-used schemes: hard and soft (kernel) coding of visual words with average-pooling. The effectiveness of these formulations is evaluated on the 15 Scenes and Caltech 10 benchmarks.
Irregular Interval Valued Fuzzy Graphs
Venue: arXiv cs.DM 2014
Year: 2014

In this paper, we define irregular interval-valued fuzzy graphs and their various classifications. Size of regular interval-valued fuzzy graphs is derived. The relation between highly and neighbourly irregular interval-valued fuzzy graphs are established. Some basic theorems related to the stated graphs have also been presented.
Feasibility of Simultaneous Information and Energy Transfer in LTE-A Small Cell Networks
Venue: arXiv cs.NI 2014
Year: 2014

Simultaneous information and energy transfer is attracting much attention as an effective method to provide green energy supply for mobiles. However the very low power level of the harvested energy from RF spectrum limits the application of such technique. Thanks to the improvement of sensitivity and efficiency of RF energy harvesting circuit as well as the dense deployment of small cells base stations, the SIET becomes more practical. In this paper, we propose a unified receiver model for SIET in LTE-A small cell base staion networks, formulate the feasibility problem with Poisson point process model and analysis the feasibility for a special and practical senario. The results shows that it is feasible for mobiles to charge the secondary battery wih harvested energy from BSs, but it is still infeasible to directly charge the primary battery or operate without any battery at all.
About the size of Google Scholar: playing the numbers
Venue: arXiv cs.DL 2014
Year: 2014

The emergence of academic search engines (Google Scholar and Microsoft Academic Search essentially) has revived and increased the interest in the size of the academic web, since their aspiration is to index the entirety of current academic knowledge. The search engine functionality and human search patterns lead us to believe, sometimes, that what you see in the search engine's results page is all that really exists. And, even when this is not true, we wonder which information is missing and why. The main objective of this working paper is to calculate the size of Google Scholar at present (May 2014). To do this, we present, apply and discuss up to 4 empirical methods: Khabsa & Giles's method, an estimate based on empirical data, and estimates based on direct queries and absurd queries. The results, despite providing disparate values, place the estimated size of Google Scholar in about 160 million documents. However, the fact that all methods show great inconsistencies, limitations and uncertainties, makes us wonder why Google does not simply provide this information to the scientific community if the company really knows this figure.
An Energy Efficient MAC Protocol for Fully Connected Wireless Ad Hoc Networks
Venue: arXiv cs.NI 2014
Year: 2014

Energy efficiency is an important performance measure of wireless network protocols, especially for battery-powered mobile devices such as smartphones. This paper presents a new energy-efficient medium access control (MAC) scheme for fully connected wireless ad hoc networks. The proposed scheme reduces energy consumption by putting radio interfaces in the sleep state periodically and by reducing transmission collisions, which results in high throughput and low packet transmission delay. The proposed MAC scheme can also address the energy saving in realtime traffics which require very low packet transmission delay. An analytical model is established to evaluate the performance of the proposed MAC scheme. Analytical and simulation results demonstrate that the proposed scheme has a significantly lower power consumption, achieves substantially higher throughput, and has lower packet transmission delay in comparison with existing power saving MAC protocols.
A Fuzzy Differential Evolution Algorithm for Job Scheduling on Computational Grids
Venue: arXiv cs.DC 2014
Year: 2014

Grid computing is the recently growing area of computing that share data, storage, computing across geographically dispersed area. This paper proposes a novel fuzzy approach using Differential Evolution (DE) for scheduling jobs on computational grids. The fuzzy based DE generates an optimal plan to complete the jobs within a minimum period of time. We evaluate the performance of the proposed fuzzy based DE algorithm with Genetic Algorithm (GA), Simulated Annealing (SA), Differential Evolution and fuzzy PSO. Experimental results have shown that the new algorithm produces more optimal solutions for the job scheduling problems compared to other algorithms.
How to Choose the Relevant MAC Protocol for Wireless Smart Parking Urban Networks?
Venue: arXiv cs.NI 2014
Year: 2014

Parking sensor network is rapidly deploying around the world and is regarded as one of the first implemented urban services in smart cities. To provide the best network performance, the MAC protocol shall be adaptive enough in order to satisfy the traffic intensity and variation of parking sensors. In this paper, we study the heavy-tailed parking and vacant time models from SmartSantander, and then we apply the traffic model in the simulation with four different kinds of MAC protocols, that is, contention-based, schedule-based and two hybrid versions of them. The result shows that the packet interarrival time is no longer heavy-tailed while collecting a group of parking sensors, and then choosing an appropriate MAC protocol highly depends on the network configuration. Also, the information delay is bounded by traffic and MAC parameters which are important criteria while the timely message is required.
Solving container terminals problems using computer-based modeling
Venue: arXiv cs.OH 2014
Year: 2014

This paper addresses the applications of different techniques for solving container terminals problems. We have built a simulation model that can be used to analyze the performance of container terminal operations. The proposed approach is intended to be applied for a real case study in Alexandria Container Terminal (ACT) at Alexandria port. The implementation of our approach shows that a proposed model increases the efficiency of Alexandria container terminal at Alexandria port.
Hybrid Systems and Control With Fractional Dynamics (II): Control
Venue: arXiv cs.SY 2014
Year: 2014

No mixed research of hybrid and fractional-order systems into a cohesive and multifaceted whole can be found in the literature. This paper focuses on such a synergistic approach of the theories of both branches, which is believed to give additional flexibility and help the system designer. It is part II of two companion papers and focuses on fractional-order hybrid control. Specifically, two types of such techniques are reviewed, including robust control of switching systems and different strategies of reset control. Simulations and experimental results are given to show the effectiveness of the proposed strategies. Part I will introduce the fundamentals of fractional-order hybrid systems, in particular, modelling and stability of two kinds of such systems, i.e., fractional-order switching and reset control systems.
Conflict-Driven XOR-Clause Learning (extended version)
Venue: arXiv cs.LO 2014
Year: 2014

Modern conflict-driven clause learning (CDCL) SAT solvers are very good in solving conjunctive normal form (CNF) formulas. However, some application problems involve lots of parity (xor) constraints which are not necessarily efficiently handled if translated into CNF. This paper studies solving CNF formulas augmented with xor-clauses in the DPLL(XOR) framework where a CDCL SAT solver is coupled with a separate xor-reasoning module. New techniques for analyzing xor-reasoning derivations are developed, allowing one to obtain smaller CNF clausal explanations for xor-implied literals and also to derive and learn new xor-clauses. It is proven that these new techniques allow very short unsatisfiability proofs for some formulas whose CNF translations do not have polynomial size resolution proofs, even when a very simple xor-reasoning module capable only of unit propagation is applied. The efficiency of the proposed techniques is evaluated on a set of challenging logical cryptanalysis instances.
Fuzzy inference system for integrated VVC in isolated power systems
Venue: arXiv cs.AI 2014
Year: 2014

This paper presents a fuzzy inference system for integrated volt/var control (VVC) in distribution substations. The purpose is go forward to automation distribution applying conservation voltage reduction (CVR) in isolated power systems where control capabilities are limited. A fuzzy controller has been designed. Working as an on-line tool, it has been tested under real conditions and it has managed the operation during a whole day in a distribution substation. Within the limits of control capabilities of the system, the controller maintained successfully an acceptable voltage profile, power factor values over 0,98 and it has ostensibly improved the performance given by an optimal power flow based automation system. CVR savings during the test are evaluated and the aim to integrate it in the VVC is presented.
Aber-OWL: a framework for ontology-based data access in biology
Venue: arXiv cs.DB 2014
Year: 2014

Many ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL). Computational access to the knowledge contained within these ontologies relies on the use of automated reasoning. We have developed the Aber-OWL infrastructure that provides reasoning services for bio-ontologies. Aber-OWL consists of an ontology repository, a set of web services and web interfaces that enable ontology-based semantic access to biological data and literature. Aber-OWL is freely available at http://aber-owl.net.
Accelerating Fast Fourier Transforms Using Hadoop and CUDA
Venue: arXiv cs.DC 2014
Year: 2014

There has been considerable research into improving Fast Fourier Transform (FFT) performance through parallelization and optimization for specialized hardware. However, even with those advancements, processing of very large files, over 1TB in size, still remains prohibitively slow. Analysts performing signal processing are forced to wait hours or days for results, which results in a disruption of their workflow and a decrease in productivity. In this paper we present a unique approach that not only parallelizes the workload over multi-cores, but distributes the problem over a cluster of graphics processing unit (GPU)-equipped servers. By utilizing Hadoop and CUDA, we can take advantage of inexpensive servers while still exceeding the processing power of a dedicated supercomputer, as demonstrated in our result using Amazon EC2.
Search Space Engine Optimize Search Using FCC_STF Algorithm in Fuzzy Co-Clustering
Venue: arXiv cs.IR 2014
Year: 2014

Fuzzy co-clustering can be improved if we handle two main problem first is outlier and second curse of dimensionality .outlier problem can be reduce by implementing page replacement algorithm like FIFO, LRU or priority algorithm in a set of frame of web pages efficiently through a search engine. The web page which has zero priority (outlier) can be represented in separate slot of frame. Whereas curse of dimensionality problem can be improved by implementing FCC_STF algorithm for web pages obtain by search engine that reduce the outlier problem first. The algorithm FCCM and FUZZY CO-DOK are compared with FCC_STF algorithm with merit and demerits on the bases of different fuzzifier used. FCC_STF algorithm in which fuzzifier fused into one entity who have shown high performance by experiment result of values (A1,B1,Vcj,A2,B2) seem to less sensitive to local maxima and obtain optimization search space in 2-D for web pages by plotting graph between J(fcc_stf) and Vcj.